Question: which is the appropriate way to use lmFit in my case?

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sabrina.shao •

**220**wrote:Hello, everyone:
I have the following experiment that I want to conduct, but I am not
sure
which is the right way to use design matrix and contrasts. Here is the
experiment:
say I have 3 different strains that are genetically different, A, B
and C
where A is the control. I also have two different treatments,
T1 and T2. For each strain, I have 4 arrays for each treatment, so in
total, I have 24 arrays. What I want to find out is the significantly
differentially expressed genes for the following comparison:
1) for control strain A: T1 vs T2
2)under T1, B vs. A (control)
3) under T1, C vs. A
4) for B, T1 vs T2
5) for C, T1 vs T2
6) interaction term of A and B , T1 and T2
7) interaction term of A and C, T1 and T2.
There are two ways I could use lmFit
One is:
for the design matrix, I use the following code:
A_T1, A_T2, B_T1, B_T2, C_T1, C_T2
sample1: 1 ,0 ,0, 0, 0 , 0
sample2 :
Then make a contrast matrix and follow the code below:
fitGene<-lmFit(gene,design=design,weights=arrayWt);
fitGene2<-contrasts.fit(fitGene,cont.matrix)
fitGene2<-eBayes(fitGene2,proportion=p);
Two:
Instead of using all samples at one time to fit into a lmFit function,
I use
two design matrix only involves A and B, T1 and T2,
and second design matrix that involves A and C, T1 and T2, and make
contrast
matrix and fit separately.
The question I have is: which one is the right one? For the first
method, I
will have larege DOF , and much lower p-values, but it was the same
test, am
I correct? Thanks for your help!
Sabrina
--
Sabrina
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